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This repository was archived by the owner on Feb 1, 2024. It is now read-only.
This repository was archived by the owner on Feb 1, 2024. It is now read-only.

Clarifying Galv's role #118

@mjaquiery

Description

@mjaquiery

Outcomes of the meeting to discuss this on July 12th:
🧔 Brady Planden
🧔 Matt Jaquiery

We discussed what Galv should be, and what the benefits of using it will be.
We envision Galv as a "Metadata Secretary", i.e. a (meta)data platform that prompts users to enter high-quality metadata which can then be provided to users at analysis time.
It should serve the needs of both individual researchers and lab managers.

  • Frontend updates
    • Tiered directory setup that mimics a cycler or standard PC directory
      • Decompose monitored paths into subdirectories
    • Individual dashboard of tasks (metadata entry) awaiting completion
      • New datasets
      • Completed/total
        • Completed to a particular standard defined by various JSON schemas
    • Group dashboard to show Harvester operators (lab manager/PI) how stuff is going
    • Final data inspection page that displays the dataset (i.e. how the current inspect element works)
    • Move to a more page-by-page view with links between
      • Closer functionally to the django-rest-framework frontend but React pretty
    • Build monitored paths a directories on landing page with the ability to "subscribe" to read only access & request write access
  • Backend updates
    • Monitored Paths for gating access
      • Created by Harvester users
      • Path is non-editable (destroy/create new if necessary)
      • MPs have admin/write/read permissions
      • At least one admin chosen at creation time
      • Userset modifiable by Harvester admin and MP admin
    • Orphan file/dataset views for Harvester users/admins
  • Benefits to users who do their homework
    - Lots of work to be done making Harvester parsers work to collect this
    - Join together different data sources
    - experiment schedule
    - equipment details
    - cell info
    - (see Battery Intelligence Lab examples)
    - Scrape several data sources automatically/parse files
    - Example scripts that pull this together
    - Example datasets with example workflows
  • Benefits to labs whose members do their homework
    • Quick oversight of historical data
    • Metaanalysis opportunities
    • Ability to track differences in e.g. cell parameters over time
  • Benefits to wider community
    • Improve collaboration
    • Improve data sharing
    • Allow large-scale analysis
    • Improve adherence to standards e.g. EMMO JSON-LD

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